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July 7, 2017 20:34
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| # https://keras.io/getting-started/sequential-model-guide/ | |
| from __future__ import print_function | |
| import keras | |
| from keras.models import Sequential | |
| from keras.layers import Dense, Dropout, Activation, Flatten | |
| import pandas as pd | |
| data = pd.read_csv("./data/train.csv") | |
| data_set = data.as_matrix() | |
| x_data = data_set[:,1:] | |
| y_data = data_set[:,0] | |
| num_classes = 10 | |
| b = int(x_data.shape[0]*0.8) | |
| x_train, x_test = x_data[:b], x_data[b:] | |
| y_train, y_test = y_data[:b], y_data[b:] | |
| print(x_train.shape) | |
| print(x_test.shape) | |
| y_train = keras.utils.to_categorical(y_train, num_classes) | |
| y_test = keras.utils.to_categorical(y_test, num_classes) | |
| model = Sequential() | |
| # now the model will take as input arrays of shape (*, 16) | |
| # and output arrays of shape (*, 32) | |
| # after the first layer, you don't need to specify | |
| # the size of the input anymore: | |
| model.add(Dense(500, input_shape=(784,))) | |
| model.add(Activation('sigmoid')) | |
| model.add(Dense(300)) | |
| model.add(Activation('sigmoid')) | |
| model.add(Dense(120)) | |
| model.add(Activation('sigmoid')) | |
| model.add(Dense(num_classes)) | |
| model.add(Activation('softmax')) | |
| # mse | |
| model.compile(optimizer='rmsprop', | |
| loss='categorical_crossentropy', | |
| metrics=['accuracy']) | |
| x_train = x_train.astype('float32') | |
| x_train /= 255 | |
| x_test = x_test.astype('float32') | |
| x_test /= 255 | |
| model.fit(x_train, y_train, epochs=20, batch_size=32, validation_data=(x_test, y_test), shuffle=True) | |
| print('saving model') | |
| model.save('nn_dence.h5') | |
| model.save_weights('nn_dence_weights.h5') | |
| print('model saved') |
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